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Portfolio selection based on predictive joint return distribution

Author

Listed:
  • Cuixia Jiang
  • Xiaoyi Ding
  • Qifa Xu
  • Xi Liu
  • Yezheng Liu

Abstract

A predictive joint return distribution can provide more useful information than moment-based risk measures in portfolio selection. This article develops a D-vine copula-CAViaR method to estimate and predict the joint probability distribution of multiple financial returns. Furthermore, we construct a portfolio model via the generalized Omega ratio inferred from the predicted joint return distribution. The superiority of our method is illustrated through an empirical application on five international stock market indices.

Suggested Citation

  • Cuixia Jiang & Xiaoyi Ding & Qifa Xu & Xi Liu & Yezheng Liu, 2019. "Portfolio selection based on predictive joint return distribution," Applied Economics, Taylor & Francis Journals, vol. 51(2), pages 196-206, January.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:2:p:196-206
    DOI: 10.1080/00036846.2018.1494812
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    Cited by:

    1. Wu, Xu & Zhang, Linlin & Li, Jia & Yan, Ruzhen, 2021. "Fractal statistical measure and portfolio model optimization under power-law distribution," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.

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